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Gemma 4 26B A4B API: toman pricing and code

google/gemma-4-26b-a4b-it

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
12,810 toman
Output · per 1M tokens
38,430 toman
One 1,000-word request ≈
67 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 6,578 toman per 1M tokens.Pricing and top-ups

Gemma 4 26B A4B example: reading an image into JSON

The example is picked from this model's own capabilities. Drop in your key and it runs as is.

# tip: a data URI works too — base64 the file and prefix it with data:image/jpeg;base64,
curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemma-4-26b-a4b-it",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Return only the invoice number and the total, as JSON."},
        {"type": "image_url", "image_url": {"url": "https://example.com/factor.jpg"}}
      ]
    }]
  }'
import base64, json
from openai import OpenAI

client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")

img = base64.b64encode(open("factor.jpg", "rb").read()).decode()

resp = client.chat.completions.create(
    model="google/gemma-4-26b-a4b-it",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Return the invoice number, the date and the total as JSON."},
            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img}"}},
        ],
    }],
    response_format={"type": "json_object"},
)
print(json.loads(resp.choices[0].message.content))
import OpenAI from "openai";
import { readFileSync } from "node:fs";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const img = readFileSync("factor.jpg").toString("base64");

const resp = await client.chat.completions.create({
  model: "google/gemma-4-26b-a4b-it",
  messages: [{
    role: "user",
    content: [
      { type: "text", text: "Return the invoice number, the date and the total as JSON." },
      { type: "image_url", image_url: { url: `data:image/jpeg;base64,${img}` } },
    ],
  }],
  response_format: { type: "json_object" },
});
console.log(JSON.parse(resp.choices[0].message.content));

What is Gemma 4 26B A4B good for?

Gemma 4 26B A4B comes from Google; in uttapen you reach it with the model id "google/gemma-4-26b-a4b-it". It accepts up to 262,144 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 32,768 tokens. On price it sits in the "very cheap" band — cheaper than 381 and dearer than 17 of the other paid models in the catalogue.

What you get on top of text in, text out: it reads images directly, which makes it a real option for invoices, forms and screenshots; it supports tool calling, so it can invoke your own functions with valid arguments; it returns schema-valid JSON through response_format, ready to hand to your code; it has a reasoning mode that pays off on multi-step problems, maths and debugging. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper. Keep in mind that reasoning tokens are output tokens and do appear on the bill.

Input runs at 12,810 toman per 1M tokens and output at 38,430 — output costs 3× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 6,578 toman per 1M, which matters a lot if your system prompt is long. You are always charged for the usage the request actually reported, never for the estimate, and a failed request costs nothing.

The closest alternative with the same capabilities from a different provider is Ministral 3 3B 2512: Gemma 4 26B A4B works out about 23% cheaper, and its context window is larger. Both run on the same key and the same code, so trying the other one is a single string change.

To make the figure concrete: 100,000 toman of credit buys roughly 1,493 thousand-word requests on Gemma 4 26B A4B, and every 1,000 toman is about 14,925 words of round trip. A job with one million input tokens and one million output tokens comes to 51,240 toman in total. Filling this model's 262,144-token window costs 3,358 toman on the input side alone, which is the real reason to keep conversation history short.

This id gets confused with "google/gemma-4-26b-a4b-it:free", because the underlying model is the same. The difference is the suffix: no suffix (the standard variant) against "free". On parameters, this one takes frequency_penalty, logit_bias, logprobs, max_completion_tokens.

Google has 43 models in our catalogue; the cheapest is Gemma 4 26B A4B at 67 toman per thousand words and the dearest Gemini 3.1 Pro Preview Custom Tools at 6,301. Among the less common parameters it accepts logit_bias, logprobs, max_completion_tokens, min_p, presence_penalty, repetition_penalty — all through the standard request body. By context size the nearest option from another provider is Aion 3.5 at 262,144 tokens.

Where it makes sense: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, pulling text and fields out of images, agents that reach out to APIs and databases, extracting data against a fixed schema, analysing a long document or codebase in one request.

Provider's own description

Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — delivering near-31B quality at...

Three real jobs, priced on this model

Each figure is derived from the prices above and moves when they do.

JobTokensCost
One chat turn with a medium history1,500 in + 400 out35 toman
Summarising a ten-page document4,000 in + 600 out74 toman
Classifying a thousand short rows120,000 in + 20,000 out2,306 toman

Other variants of this model

Same core model, different execution terms and different price. This page is the standard variant.

VariantModel idOutput / 1MContext
freegoogle/gemma-4-26b-a4b-it:free0262,144

Put the variant's id verbatim in the model field; nothing else in your code changes.

Price history

DateInput (toman/1M)Output (toman/1M)
5 Nov 202612,81038,430
5 Nov 202623,19769,244
5 Nov 202618,35055,395
5 Nov 202612,46437,738
5 Nov 20268,65525,620
5 Nov 20269,00227,005
5 Nov 20269,34828,044
5 Nov 202611,07932,891
5 Nov 202612,46437,045
5 Nov 202610,38731,160
5 Nov 202611,77135,314
5 Nov 202617,31151,587

Every price change for this model is recorded. Toman figures use today's rate.

Frequently asked

How do I call Gemma 4 26B A4B from Iran?
Sign up with your mobile number, top the wallet up in toman, create an API key, then in the official OpenAI SDK point base_url at https://api.uttapen.ir/v1 and set model to "google/gemma-4-26b-a4b-it". Nothing else in your code changes.
What does Gemma 4 26B A4B cost in toman?
12,810 toman per 1M input tokens and 38,430 toman per 1M output tokens; a 1,000-word request is around 67 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Gemma 4 26B A4B take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 32,768 tokens.
Does Gemma 4 26B A4B support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Image input is accepted through image_url, as a data URI or a public URL. Structured output through response_format works too.